Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes

cg.contactJennifer.Woodward@ars.usda.goven_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerInternational Livestock Research Institute - ILRIen_US
cg.contributor.centerEthiopian Institute of Agricultural Research - EIARen_US
cg.contributor.centerFood and Agriculture Organization of the United Nations - FAOen_US
cg.contributor.centerLilongwe University of Agriculture & Natural Resources - LUANARen_US
cg.contributor.centerCornell University - CORNELLen_US
cg.contributor.centerAssociation for Strengthening Agricultural Research in Eastern and Central Africa - ASARECAen_US
cg.contributor.centerUnited States Department of Agriculture, Agricultural Research Service - USDA-ARSen_US
cg.contributor.centerUniversity of Natural Resources and LIfe Science - BOKUen_US
cg.contributor.centerVirginia State University - VSUen_US
cg.contributor.centerMinistry of Livestock and Fisheries -Tanzaniaen_US
cg.contributor.centerNelson Mandela African Institute of Science and Technology - NM - AISTen_US
cg.contributor.centerNational Research Center - NRCen_US
cg.contributor.centerNational Animal Genetic Resources Centre and DataBank - NAGRC & DBen_US
cg.contributor.centerGeorge Mason University, College of Science, School of Physics, Astronomy, and Computational Sciences - GMU-CoS-SPACSen_US
cg.contributor.centerAcceligen Incen_US
cg.contributor.centerGeorge Mason University, College of Science, School of Systems Biology College of Science - GMU-CoS-SSBCSen_US
cg.contributor.centerNational Center for Applied Research in Rural Development - CENRADERUen_US
cg.contributor.centerAnimal Production Research Center, Department of Animal Genetic Resources Development - APRC-DAGRDen_US
cg.contributor.centerInstitut d’Économie Ruraleen_US
cg.contributor.centerMozambique Institute of Agricultural Research, Directorate of Animal Science - IIAM-DASen_US
cg.contributor.crpResilient Agrifood Systems - RAFSen_US
cg.contributor.funderUnited States Department of Agriculture, Agricultural Research Service - USDA-ARSen_US
cg.contributor.initiativeSustainable Animal Productivityen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.creator.idGetachew, Tesfaye: 0000-0002-0544-6314en_US
cg.creator.idHaile, Aynalem: 0000-0001-5914-0487en_US
cg.creator.idRischkowsky, Barbara: 0000-0002-0035-471Xen_US
cg.identifier.doihttps://dx.doi.org/10.3389/fgene.2023.1200770en_US
cg.isijournalISI Journalen_US
cg.issn1664-8021en_US
cg.journalFrontiers in Geneticsen_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.impactAreaNutrition, health and food securityen_US
cg.subject.impactAreaPoverty reduction, livelihoods and jobsen_US
cg.subject.sdgSDG 1 - No povertyen_US
cg.subject.sdgSDG 13 - Climate actionen_US
cg.volume14en_US
dc.contributorKinser, Jasonen_US
dc.contributorHuson, Heatheren_US
dc.contributorSonstegard, Taden_US
dc.contributorSölkner, Johannen_US
dc.contributorVaisman, Iosifen_US
dc.contributorBoettcher, Paulen_US
dc.contributorMasiga, Clet Wanduien_US
dc.contributorMukasa, Christopheren_US
dc.contributorGuangul, Solomonen_US
dc.contributorAgaba, Morrisen_US
dc.contributorAhmed, Saharen_US
dc.contributorMaminiaina, Oliveren_US
dc.contributorGetachew, Tesfayeen_US
dc.contributorGondwe, Timothyen_US
dc.contributorHaile, Aynalemen_US
dc.contributorHassan, Yassiren_US
dc.contributorKihara, Absolomonen_US
dc.contributorKouriba, Alyen_US
dc.contributorMruttu, Hassanen_US
dc.contributorMujibi, Denisen_US
dc.contributorNandolo, Wilsonen_US
dc.contributorRischkowsky, Barbaraen_US
dc.contributorD. Rosen, Benjaminen_US
dc.contributorSayre, Brianen_US
dc.contributorTaela, Mariaen_US
dc.contributorVan Tassell, Curtis P.en_US
dc.creatorWoodward-Greene, Jenniferen_US
dc.date.accessioned2023-12-29T16:06:15Z
dc.date.available2023-12-29T16:06:15Z
dc.description.abstractIntroduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day’s images, or even an entire sampling trip’s images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.en_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/fb0760256d555f3fab5bd26e4c95cdbe/v/451fe3fee59cf8555ada45167a9cc19een_US
dc.identifier.citationJennifer Woodward-Greene, Jason Kinser, Heather Huson, Tad Sonstegard, Johann Sölkner, Iosif Vaisman, Paul Boettcher, Clet Wandui Masiga, Christopher Mukasa, Solomon Guangul, Morris Agaba, Sahar Ahmed, Oliver Maminiaina, Tesfaye Getachew, Timothy Gondwe, Aynalem Haile, Yassir Hassan, Absolomon Kihara, Aly Kouriba, Hassan Mruttu, Denis Mujibi, Wilson Nandolo, Barbara Rischkowsky, Benjamin D. Rosen, Brian Sayre, Maria Taela, Curtis P. Van Tassell. (6/9/2023). Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes. Frontiers in Genetics, 14.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/68957
dc.languageenen_US
dc.publisherFrontiers Mediaen_US
dc.rightsCC-BY-4.0en_US
dc.sourceFrontiers in Genetics;14,(2023)en_US
dc.subjectcommunity-based breeding programen_US
dc.subjectafrican goat improvement networken_US
dc.subjectimage collection protocolen_US
dc.subjectlivestock phenotypesen_US
dc.titleUsing the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network—Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypesen_US
dc.typeJournal Articleen_US
dcterms.available2023-09-06en_US
dcterms.issued2023-09-06en_US
mel.impact-factor3.7en_US

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